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Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia

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Listed:
  • Vivi Alatas
  • Abhijit Banerjee
  • Arun G. Chandrasekhar
  • Rema Hanna
  • Benjamin A. Olken

Abstract

We use unique data from 600 Indonesian communities on what individuals know about the poverty status of others to study how network structure influences information aggregation. We develop a model of semi-Bayesian learning on networks, which we structurally estimate using within-village data. The model generates qualitative predictions about how cross-village patterns of learning relate to different network structures, which we show are borne out in the data. We apply our findings to a community-based targeting program, where villagers chose which households should receive aid, and show that networks the model predicts to be more diffusive differentially benefit from community targeting.

Suggested Citation

  • Vivi Alatas & Abhijit Banerjee & Arun G. Chandrasekhar & Rema Hanna & Benjamin A. Olken, 2012. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," NBER Working Papers 18351, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18351
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    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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